The number one barrier to adopting AI in complex systems.

AI has demonstrated its revolutionary potential in a variety of areas, from forecasting traffic patterns to optimising energy use and expediting transportation. However, implementing AI is more complicated for many businesses that work with intricate, real-world systems than just plugging in a model and hoping for the best. Many AI projects don’t scale or provide noticeable results, despite the hype. Why? The most obvious obstacle is data that is disjointed, unstructured, and fractured.

Data flows from a number of sources in settings such as supply chains, ports, cities, and energy grids, including IoT sensors, operational systems, external feeds, and legacy infrastructure. However, this data is rarely interoperable, consolidated, or standardised. It lacks real-time accessibility, operates in silos, and frequently lacks context. This fundamental issue must be resolved before AI can even start to provide insights.

We have repeatedly witnessed this difficulty at Entopy. By their very nature, complex systems are dynamic, with many stakeholders, real-time demands, and unpredictable variables. The data problem must be resolved before we can create AI that functions in these settings. Because of this, Entopy’s platform is made to combine fragmented, complicated, and real-time data and transform it into a contextualised, structured stream of information that can be utilised to inform intelligence.

Ontology is essential in this situation. A framework that outlines the relationships between various data elements is called an ontology. Giving data structure and meaning is more important than merely marking it, as this enables machines to comprehend and make cross-domain connections. You can connect a freight arrival time to vessel schedules, port capacity, and traffic information on the roads – all inside a single, real-time system – if you have a solid ontology in place. Because of this, AI is not only feasible but also trustworthy and applicable.

Micromodels, which are specialist AI models aimed at particular system tasks, are another component of the solution. By focussing on specific operational difficulties, micromodels provide agility and precision in contrast to monolithic models that aim to comprehend everything at once. Our micromodels at Entopy can be coordinated to provide system-wide insights after being trained on structured, contextualised data. Performance and flexibility are significantly increased by this method, particularly in settings where circumstances change quickly.

Accessibility is the last puzzle component. Data scientists shouldn’t be the only ones with access to AI. Entopy makes sure that operators, planners, and stakeholders can quickly understand and act upon insights by providing predictive and operational intelligence through intuitive digital twins, bridging the gap between data and decision.

We must cease considering data infrastructure as an afterthought if we are to fully realise AI’s potential in complex systems. Scalable AI is built on a foundation of contextual, linked, and structured data. Additionally, with the correct architecture, from digital twins to ontologies and micromodels, AI turns become a potent operational ally rather than merely a tool.

We’re not only using AI at Entopy. In order to make better, quicker judgements in the real world, we are assisting businesses in overcoming the very obstacles that stand in their way.